Big Data Solutions For Business Analytics

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It starts with what you see—not right in front of you, but rather, far ahead. Because what really matters is where you’re going. Our Connected Data Platforms can help you create actionable intelligence to transform your business with: Speed. Insight. Flexibility. Affordability.

See how various industries are using the Hortonworks connected data platform to achieve amazing things. Will you be the next one to be first in some way? We don’t see why not.

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Way to increase Business your business

Exactly how do significant associations utilize information and examination to educate key and operational choices? Senior pioneers give knowledge into the difficulties and openings.

Hardly any debate that associations have more information than any other time in recent memory available to them. In any case getting significant bits of knowledge from that information—and changing over learning without hesitation—is less demanding said than done. We talked with six senior pioneers from significant associations and got some information about the difficulties and openings engaged with embracing progressed investigation: Murli Buluswar, boss science officer at AIG; Vince Campisi, boss data officer at GE Software; Ash Gupta, boss hazard officer at American Express; Zoher Karu, VP of worldwide client advancement and information at eBay; Victor Nilson, senior VP of huge information at AT&T; and Ruben Sigala, boss examination officer at Caesars Entertainment. An altered transcript of their remarks takes after.

The greatest test of influencing the development from a knowing society to a figuring out how to culture—from a culture that generally relies upon heuristics in basic leadership to a culture that is significantly more target and information driven and grasps the intensity of information and innovation—is truly not the cost. At first, it to a great extent winds up being creative ability and latency.

What I have discovered in my most recent couple of years is that the intensity of dread is very enormous in developing oneself to think and act contrastingly today, and to make inquiries today that we weren't getting some information about our parts previously. Also, it's that outlook change—from a specialist based mentality to one that is significantly more unique and considerably more learning focused, rather than a settled attitude—that I believe is principal to the economical wellbeing of any organization, extensive, little, or medium

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How Big Data Used in the Manufacturing Industry

Big Data Use Cases in the Manufacturing Industry

Big data has applications in just about every industry – retail, healthcare, financial services, government. Any organization that can assimilate data to answer nagging questions about their operations can benefit from big data. All big data projects start with a viable use case. Here are four sample big data use cases for the manufacturing industry.

Analyzing big data use cases in the manufacturing industry can reduce processing flaws, improve production quality, increase efficiency, and save time and money. Tata Consultancy Services asked manufacturers to rate the following big data benefits on a scale of one to five:

Product quality and defects tracking – 3.37
Supply planning – 3.34
Manufacturing process defect tracking – 3.32
Supplier, components, and parts defect tracking – 3.11
Supplier performance data to inform contract negotiations – 3.08
Output forecasting – 3.03
Increasing energy efficiency – 2.97
Testing and simulation of new manufacturing processes – 2.88
Support for mass-customization of manufacturing – 2.75
The range of big data use cases in the manufacturing industry is limited only by available data and imagination.

Why Big Data Use Cases in the Manufacturing Industry?

Before looking at some specific big data use cases in the manufacturing industry, let's address the role use cases play in big data analytics.

Unless you narrow your query to a specific business challenge that can be revealed by patterns or examples, you won’t get much value from big data. Just having vast quantities of data at hand for analysis doesn’t mean you can extract the insight you need. Use cases force you to narrowly define the question.

A big data use case provides a focus for analytics, providing parameters for the types of data that can be of value and determining how to model that data using Hadoop analytics. For example, answering a question such as “where is the next big market for my product” is harder to answer than “who is likely to buy more product in the United States.” Here are four sample big data use cases in the manufacturing industry:

1. Improving Manufacturing Processes

McKinsey and Company offers a big data use case in pharmaceutical manufacturing. A biopharmaceutical company was using live, genetically engineered cells and tracking 200 variables to track the purity of its manufacturing process for vaccines and blood components. However, two batches of the same substance manufactured using identical processes showed a yield variation from 50 to 100 percent. The inconsistency in capacity and quality could attract regulatory attention.

The project team segmented its manufacturing processes into clusters of activity. Using big data analytics the team assessed process interdependencies and identified nine parameters that had a direct impact on vaccine yield. By modifying target processes the company was able to increase vaccine production by 50 percent resulting in savings between $5 and $10 million annually.

2. Custom Product Design

Tata Consultancy Services cites the case of a $2 billion company that generates most of its revenue by manufacturing products to order.

Using big data analytics this company was able to analyze the behavior of repeat customers. The outcome is critical to understanding how to deliver goods in a timely and profitable manner.

Much of the analyses centered on how to make sure strong contracts were in place. The company also was able to shift to lean manufacturing to determine which products were viable and which ones needed to be scrapped.

3. Better Quality Assurance

Intel has been harnessing big data for its processor manufacturing for some time. The chipmaker has to test every chip that comes off its production line. That normally means running each chip through 19,000 tests.

Using big data for predictive analytics Intel was able to significantly reduce the number of tests required for quality assurance. Starting at the wafer level, Intel analyzed data from the manufacturing process to cut down test time and focus on specific tests.

The result was a savings of $3 million in manufacturing costs for a single line of Intel Core processors. By expanding big data use in its chip manufacturing, the company expects to save an additional $30 million.

4. Managing Supply Chain Risk

One manufacturer is using big data to reduce risk in delivery of raw materials, no matter what happens in the supply chain.

Using big data analytics, the company has overlaid potential delays on a map, analyzing weather statistics for tornadoes, earthquakes, hurricanes, etc. Predictive analytics allow the company to calculate the probabilities of delays. The company uses the analytics findings to identify backup suppliers and develop contingency plans to make sure production isn’t interrupted by natural disaster.

These are just four examples of big data use cases in the manufacturing industry. There are dozens of others. If you can narrowly define the problem and assemble the right data you can harness big data to address almost any manufacturing problem.

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How bigdata supports Health care

Big data is all about delivering big insights: gathering information from disparate sources and analyzing it to reveal trends that are not accessible in any other way. Of all the industries that are finding value from big data analytics, healthcare has the potential to realize the greatest returns. Not only can big data show healthcare providers how to increase profitability and improve operational efficiency, big data also has the potential to uncover trends that can directly improve people’s well-being

Analyzing Electronic Health Records (EHRs) – Doctors sharing EHRs can aggregate and analyze data for trends that can reduce healthcare costs. Sharing data between physicians and healthcare providers as they examine patients can reduce duplicate tests and improve patient care. Most EHR data is siloed, largely for security and regulatory compliance reasons, but finding a secure way to mine patient data can improve the quality of care while reducing costs.

 Analyzing Hospital Networks – Consider the power of analyzing trends in hospital care. For example, centralizing analysis of medical instruments in a pediatric ward can isolate possible infant infection trends earlier. Or consider the case of one hospital that was able to reduce post-operative staph infection: by using big data, the administration was able to determine that one surgeon was prescribing post-operative antibiotics that were less effective against infection.

 Control Data for Public Health Research – The medical profession is drowning in data. Medical offices and hospitals submit data about medical conditions and immunizations, but without big data, those data are meaningless. Using analytics normalizes raw patient data to fill gaps in public health records that can affect regulations as well as providing better care.

 Evidence-Based Medicine – Most hospitals and emergency rooms practice “cookbook medicine,” where a patient is admitted, and the physician uses the same battery of tests in order to identify the cause for symptoms. Using evidence-based medicine, the doctor can match symptoms to a larger patient database in order to come to an accurate diagnosis faster and more efficiently. Where big data plays a role is assimilating information from different sources and normalizing the data, so one record that describes “high blood pressure” maps to another that describes “elevated blood pressure.”

Reducing Hospital Readmissions – Hospital costs are rising partially because of high readmission rates within 30 days of patient release. Using big data analytics in order to identify at-risk patients based on past history, chart information, and patient trends, hospitals can identify at-risk patients and provide the necessary care to reduce readmission rates.

Protecting Patients’ Identity – Insurers like UnitedHealthcare are using big data analytics in order to detect medical fraud and identity theft. The company uses analytics on speech-to-text records from calls to the call center to identify potential fraudsters. The insurance company also uses big data in order to predict which types of treatment plans are more likely to succeed.

 More Efficient Medical Practice – As medical practices grow, accommodating more doctors and more patients becomes more challenging. Consider Westmed Medical Group in Westchester County, New York. This practice grew from 16 physicians in 1996 to 250 physicians today seeing 250,000 patients, with annual revenue of $285 million. As the practice grows, it needs to be more efficient in order to succeed. Using big data, the practice was able to analyze more than 2,200 processes and procedures. As a result, the practice was able to streamline workflow, shift clinical tasks from doctors to nurses, reduce unnecessary testing, and improve patient satisfaction. Like any business, big data made it clear where processes could be improved.

what is prediction in statistical techniques..?

Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions


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what is Decision Making..?

Decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several alternative possibilities. Every decision-making process produces a final choice, which may or may not prompt action.

Decision-making is the process of identifying and choosing alternatives based on the values, preferences and beliefs of the decision-maker.

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Opioid abuse takes a huge toll on human life and the problem is getting worse. Just this year, overdose from misuse of prescription or illegal opioids overtook road accidents as the most common cause of accidental death in the #US. In #Canada, the situation has been called a “national health crisis.”

It’s such a huge problem that tackling it was identified as a key priority by Barack Obama earlier , and $1.1 bn ringfenced for developing and providing solutions.

Now data scientists at non-for-profit healthcare insurance providers Blue Cross Blue Shield of Tennessee (BCBST) have entered a partnership with Big Data analytics architects at Fuzzy Logix on a data-driven project that could prove to be a key part of the solution.

The project used years’ worth of pharmacy data, along with claims data, both from BCBS’s customers and others, through a third-party sharing agreement. #Analysts quickly found they were able to highlight risk factors that could indicate whether a person could be in danger of developing an opioid abuse problem. This will hopefully allow doctors and other specialists to step in and offer preventative care before the problem gets out of hand.

By examining the data, analysts were able to determine that certain traceable behaviors – such as frequent use of different prescribers and dispensaries, in combination with each other, were predictive of a chance of later presenting with an abuse or misuse issue.

If you want to find out how Big Data is helping to make the world a better place, there’s no better example than the uses being found for it in healthcare

The last decade has seen huge advances in the amount of data we routinely generate and collect in pretty much everything we do, as well as our ability to use technology to analyze and understand it. The intersection of these trends is what we call “Big Data” and it is helping businesses in every industry to become more efficient and productive.

Healthcare is no different. Beyond improving profits and cutting down on wasted overhead, Big Data in healthcare is being used to predict epidemics, cure disease, improve quality of life and avoid preventable deaths. With the world’s population increasing and everyone living longer, models of treatment delivery are rapidly changing, and many of the decisions behind those changes are being driven by data. The drive now is to understand as much about a patient as possible, as early in their life as possible – hopefully picking up warning signs of serious illness at an early enough stage that treatment is far more simple (and less expensive) than if it had not been spotted until later.

So to take a journey through Big Data in healthcare, let’s start at the beginning – before we even get ill.

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